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Field
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Python programming, gaining valuable research experience alongside your studies. You will collaborate with an experienced, interdisciplinary research team and contribute to the development of Abaqus
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from problem definition through to validated implementation. Develop solutions using machine learning, deep learning and computer vision, and independently implement key components in Python. Develop
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) Implementation and training of an imitation learning model to replicate human delivery manipulations Evaluation of generalization capabilities across different tools and placement locations Testing on the humanoid
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. We are working across natural and social sciences in a multidisciplinary way. This approach requires new ways of integrating different types and sources of data to validate if the results or structure
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of the transfer What you contribute You are currently studying computer science, mechanical engineering, robotics or a comparable field Basic knowledge of Python, ideally with PyTorch Experience or interest in
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you contribute You are currently studying computer science, robotics, mechanical engineering or a comparable field Basic knowledge of Python, ideally with PyTorch Familiar with ROS2 Interest in multi
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monitoring regarding reaction time and situational awareness What you contribute You are currently studying computer science, mechanical engineering, robotics or a comparable field Basic knowledge of Python
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change Support for the development and integration of 3D pose estimation and localization methods in the building sector Creation of synthetic reference datasets and the development of Python scripts
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methods for the animation of 3D human avatars using Python and Pytorch Capturing of training data using Motion Capture Systems Setting up and evaluating experiments on machine learning model performance
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include: - Extension to radio and optical diagnostics to test different interaction scenarios - Extension to low mass binary interactions at optical wavelengths - Development of empirically motivated models